What are the most frequently asked questions in Python interviews?

Python basic interview questions

1. What are the Python data structures?

• Integer (int)

• Floating point (float)

• String (str)

• Boolean (bool)

• List ( list)

• Tuple (tuple)

• Dictionary (dict)

• Set )

2. What is the difference between lists and tuples in Python? Are tuples really immutable?

• List: list is a variable type, and the data can change dynamically

• Tuple: is an immutable type, with a fixed size

3. What are generators and iterators? What's the difference between them?

Iterator

Function: Simplify the loop code and save memory

It is an object that can remember the traversed position. The iterator object starts accessing from the first element of the collection until all elements have been accessed. Iterators can only go forward and not backward

Iterators have two basic methods: iter() and next().

Generator

Function: save a lot of memory

The function that uses yield is called a generator. The generator is an iterator that returns Functions can only be used for iterative operations. It is easier to understand that a generator is an iterator

Principle: During the process of calling the generator, the function will pause and save all current values ​​every time it encounters yield. Run information, return the value of yield, and continue running from the current position the next time the next() method is executed

4. What is a closure? What are decorators? What does decorator do? Have you ever used decorators? Please write an example of a decorator

Closure refers to the object obtained by packaging the languages ​​that make up functions and the execution environments of these languages ​​in Python

The decorator is a kind of added function or class functions, which can quickly insert the same functionality into different functions or classes. Syntax: "@decorator name" is added before the function. Example:

5. What is an anonymous function? What are the benefits of using anonymous functions?

Anonymous function: The function created using lambda is so-called anonymous, which means that a function is no longer defined in a standard form such as a def statement.

Benefits:

1. When using Python to write some execution scripts, using lambda can save the process of defining functions and make the code more streamlined.

2. For some abstract functions that will not be reused elsewhere, sometimes it is difficult to name the function. There is no need to consider naming issues when using lambda.

3. Use lambda to make the code easier to understand at certain times.

Application scenarios: Often used in combination with some built-in functions, such as map(), filter(), sorted(), reduce(), etc.

Expression format：lambda parameter list: lambda body

##Case：

frame.applymap(lambda x: '%.2f' % x)
frame.apply(lambda x: x.max() - x.min())

• Shallow copy, changing the value of the variable type element in the original object will affect the copied object at the same time; changing the value of the immutable type element in the original object will not affect the copied object.

• Deep copy, in addition to the top-level copy, also copies the sub-elements. After deep copying, all the variable element addresses of the original object and the copied object are no longer the same

os module: Provides many functions related to the operating system.

sys module: Common tool scripts often call command line parameters .

re module: Provides regular expression tools for advanced string processing. For complex matching and processing, regular expressions provide a concise and optimized solution:

random module: Provides tools for generating random numbers.

json module: Provides methods for Python to parse json data and convert to and from python format

time module: A module used to process time in python

logging module: module for log processing in python

xml module: module for locating html tags in python crawler

10. Python garbage collection mechanism? (Just know it)

Python adopts a reference counting mechanism as the main strategy, supplemented by two mechanisms: mark-clear and generational collection (intergenerational recycling, generational recycling)

Counting mechanism: Python's GC module mainly uses reference counting to track and recycle garbage. On the basis of reference counting, "mark-clear" can also be used to solve the problem of circular references that may occur in container objects. Further improve the efficiency of garbage collection by exchanging space for time through generational collection.

Mark-Clear:: The emergence of mark-clear breaks the circular reference, that is, it only focuses on those objects that may produce circular references. Disadvantages: The additional operations caused by this mechanism Proportional to the memory blocks that need to be reclaimed.

Generation-separated recycling Principle: All memory blocks in the system are divided into different collections according to their survival time. Each collection becomes a "generation", and the frequency of garbage collection increases with the The survival time of "generation" decreases with the increase. In other words, the longer an object lives, the less likely it is to be garbage, and the frequency of garbage collection for it should be reduced. So how to measure this survival time: It is usually measured by several garbage collection actions. If an object has gone through more garbage collections, it can be concluded that the object has a longer survival time.

The above is the detailed content of What are the most frequently asked questions in Python interviews?. For more information, please follow other related articles on the PHP Chinese website!